Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

Hussain, Muhammad; Dhimish, Mahmoud; Titarenko, Sofya; Mather, Peter

doi:10.1016/j.renene.2020.04.023

Cited by 102 publications

(37 citation statements)

References 23 publications

(24 reference statements)

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“…In 2017, Abdelgayed, Morsi, and Sidhu [136] used a PNN classifier for FD and fault diagnosis in the DC side of a PV system. In 2020, Hussain et al [137] proposed a fault detection algorithm for PV based on ANN with 97% overall accuracy. Condition monitoring in wind turbines is also important for improving maintenance by detecting faults at an early stage.…”

Section: Faults Detectionmentioning

confidence: 99%

“…Table 3 summarizes the AI techniques for power system FD. [134] 2017 Microgrid FD KNN, DT Garoudja et al [136] 2017 PV FD PNN Zhang et al [129] 2017 Line trip FD LSTM, SVM Sirojan et al [127] 2018 HIFD ANN Wang et al [131] 2018 Line trip FD AE, SVM Shafiullah et al [132] 2018 Microgrid FD ANN Helbing et al [138] 2018 WT FD ANN Baghaee et al [135] 2019 FD SVM Govar et al [128] 2019 HIFD ELM Jayamaha et al [133] 2019 Microgrid FD ANN Fazai et al [124] 2019 PV FD GPR Ashrafuzzaman et al [125] 2020 FD Ensemble Haq et al [130] 2020 Line FD ELM Hussain et al [137] 2020 PV FD ANN Niu et al [126] 2021 FD Ensemble Gunturi and Sarkar [139] 2021 Energy theft Ensemble…”

Section: Faults Detectionmentioning

confidence: 99%

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Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

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Section: Faults Detectionmentioning

confidence: 99%

Section: Faults Detectionmentioning

confidence: 99%

Artificial Intelligence Techniques in Smart Grid: A Survey

2021

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“…More specialized approaches use methods to detect line-to-line faults in several situations. For example, [43] uses a support vector machine trained with simulated data and tested in a real PV plant which achieves up to 94.74% accuracy to detect short-circuit conditions, while [44] uses a Radial Basis Function Neural Network using irradiance and power as its inputs to detect one or modules disconnections from the photovoltaic system. The system attained 98.1%, 97.9%, and 96.5% accuracy when tested in two plants, one with 2.2 kW and other with 4.16 kW when subject to normal operation, shadowing, and overcast conditions.…”

Section: Fault Classificationmentioning

confidence: 99%

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Lazzaretti

Costa

Rodrigues

et al. 2020

Sensors

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Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

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“…ANNs are computational models inspired by the human brain and they work just like a functioning human nervous system. In recent years, the ANNs have become useful and competent modelling tools, especially for modelling the environmental processes that are difficult to identify physically or statistically methodologies and controls [Seketekin et al, 2020;Hussain et al, 2020;Wanga et al, 2020].…”

Section: Introductionmentioning

confidence: 99%

Adaptive proportional integral controller based on ann for dc link voltage control single-phase inverter connected to grid

Traoré¹,

Ndiaye²,

Ba³

et al. 2021

J.P.SOAPHYS

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The output power of the inverter of a PV system is directly affected by the DC-link voltage. Hence an adaptive Proportional Integral controller based on Artificial Neural Networks is developed in this paper. MATLAB/Simulink is used for the simulation of the studied system in order to evaluate the performance of the proposed methods. Simulation results show that the proposed API-ANN is faster to track the DC-link voltage than the conventional method. The injected harmonics to the grid were significantly reduced with API-ANN (0.08 % of total harmonic distortion) in comparison with the classic PI with 4.23 %. The API-ANN gives a good performance than the classic PI.

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Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

Cited by 102 publications

References 23 publications

Artificial Intelligence Techniques in Smart Grid: A Survey

Artificial Intelligence Techniques in Smart Grid: A Survey

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Adaptive proportional integral controller based on ann for dc link voltage control single-phase inverter connected to grid

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